Figure REF , second and third columns, shows the partition estimates under the CCP-PPM (second column) and Loschi method (third column). Partition estimates were obtained using the salso [1]} R package and the generalization of the Variation of Information loss function [2]}. Since in our case it seems natural to penalize change point false positives more than false negatives, we set the false positive penalty parameter of the salso function to \(a=25\)  [3]}. We note briefly that setting \(a=25\)  was driven primarily by Loschi's method. If \(a > 25\) , then Loschi's method tended to smooth over some change points and for \(a< 25\)  it tended to produce more change points than what would be desired. However, the CCP-PPM was reasonably robust to \(a\) 's value. This is a consequence of the change point probabilities from the the Loschi method being more central (i.e., closer to 0.5) than those from the CCP-PPM.
